run_exp2a.sh

#!/bin/bash
# Run Experiment 2-A for all model types with proper conda environments
#
# Usage:
#   ./run_exp2a.sh           # Run all models
#   ./run_exp2a.sh qwen      # Run only Qwen
#   ./run_exp2a.sh nvila     # Run only NVILA
#   ./run_exp2a.sh molmo     # Run only Molmo

set -e

SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
OUTPUT_DIR="/data/shared/Qwen/experiments/exp2a_results"
SAMPLES=50  # Samples per category
SEED=42

echo "=============================================="
echo "Experiment 2-A: Embedding Space Analysis"
echo "=============================================="
echo "Output directory: $OUTPUT_DIR"
echo "Samples per category: $SAMPLES"
echo ""

# Initialize conda
source /root/miniconda3/etc/profile.d/conda.sh

# Parse arguments
RUN_QWEN=false
RUN_NVILA=false
RUN_MOLMO=false

if [ $# -eq 0 ]; then
    RUN_QWEN=true
    RUN_NVILA=true
    RUN_MOLMO=true
else
    for arg in "$@"; do
        case $arg in
            qwen) RUN_QWEN=true ;;
            nvila) RUN_NVILA=true ;;
            molmo) RUN_MOLMO=true ;;
            *) echo "Unknown model: $arg. Use qwen, nvila, or molmo." ;;
        esac
    done
fi

# Run for Qwen (no conda environment - deactivate all)
if [ "$RUN_QWEN" = true ]; then
    echo "=============================================="
    echo "Running Qwen2.5-VL experiments..."
    echo "=============================================="
    # Deactivate all conda environments to get to base system python
    conda deactivate 2>/dev/null || true
    conda deactivate 2>/dev/null || true
    conda deactivate 2>/dev/null || true

    # Use system python directly
    /root/miniconda3/bin/python "$SCRIPT_DIR/exp2a_embedding_analysis.py" \
        --model_type qwen \
        --scales vanilla 80k 400k 800k 2m \
        --output_dir "$OUTPUT_DIR" \
        --samples_per_category $SAMPLES \
        --seed $SEED
fi

# Run for NVILA (vila conda environment)
if [ "$RUN_NVILA" = true ]; then
    echo "=============================================="
    echo "Running NVILA experiments..."
    echo "=============================================="
    conda activate vila

    python "$SCRIPT_DIR/exp2a_embedding_analysis.py" \
        --model_type nvila \
        --scales vanilla 80k 400k 800k 2m \
        --output_dir "$OUTPUT_DIR" \
        --samples_per_category $SAMPLES \
        --seed $SEED

    conda deactivate
fi

# Run for Molmo (molmo conda environment)
if [ "$RUN_MOLMO" = true ]; then
    echo "=============================================="
    echo "Running Molmo experiments..."
    echo "=============================================="
    conda activate molmo

    python "$SCRIPT_DIR/exp2a_embedding_analysis.py" \
        --model_type molmo \
        --scales vanilla 80k 400k 800k \
        --output_dir "$OUTPUT_DIR" \
        --samples_per_category $SAMPLES \
        --seed $SEED

    conda deactivate
fi

echo ""
echo "=============================================="
echo "Experiments completed!"
echo "Results saved to: $OUTPUT_DIR"
echo "=============================================="

# Generate combined comparison plot (uses base python)
/root/miniconda3/bin/python - <<'EOF'
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

output_dir = "/data/shared/Qwen/experiments/exp2a_results"

# Collect all results
all_results = []
for model_type in ['qwen', 'nvila', 'molmo']:
    summary_path = os.path.join(output_dir, model_type, 'results_summary.csv')
    if os.path.exists(summary_path):
        df = pd.read_csv(summary_path)
        all_results.append(df)

if all_results:
    combined_df = pd.concat(all_results, ignore_index=True)
    combined_df.to_csv(os.path.join(output_dir, 'all_results_summary.csv'), index=False)

    # Create combined comparison plot
    pairs = ['sim_above_far', 'sim_under_close', 'sim_left_right']
    pair_labels = ['above-far', 'under-close', 'left-right']

    fig, axes = plt.subplots(1, 3, figsize=(18, 6))

    for ax, (pair, pair_label) in zip(axes, zip(pairs, pair_labels)):
        for model_type in ['qwen', 'nvila', 'molmo']:
            model_data = combined_df[combined_df['model'].str.startswith(model_type)]
            if not model_data.empty:
                scales = model_data['model'].str.replace(f'{model_type}_', '')
                values = model_data[pair].values
                ax.plot(scales, values, marker='o', label=model_type.upper(), linewidth=2)

        ax.set_title(f'{pair_label}', fontsize=12, fontweight='bold')
        ax.set_xlabel('Training Scale')
        ax.set_ylabel('Cosine Similarity')
        ax.legend()
        ax.set_ylim(0, 1)
        ax.axhline(y=0.5, color='gray', linestyle='--', alpha=0.5)
        ax.tick_params(axis='x', rotation=45)

    plt.suptitle('Spatial Concept Similarity Across Training Scales', fontsize=14, fontweight='bold')
    plt.tight_layout()
    plt.savefig(os.path.join(output_dir, 'all_models_comparison.png'), dpi=300, bbox_inches='tight')
    plt.close()

    print(f"\nCombined results saved to {output_dir}/all_results_summary.csv")
    print(f"Combined plot saved to {output_dir}/all_models_comparison.png")
else:
    print("No results found to combine.")
EOF